Overview

Dataset statistics

Number of variables55
Number of observations19329
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 MiB
Average record size in memory801.4 B

Variable types

NUM25
CAT24
DATE5
BOOL1

Warnings

EDAD_TIPO has constant value "19329" Constant
COD_MENOR has constant value "19329" Constant
EDAD_CANT has a high cardinality: 96 distinct values High cardinality
DESC_COMUNA has a high cardinality: 221 distinct values High cardinality
DIAG1 has a high cardinality: 180 distinct values High cardinality
DIAG2 has a high cardinality: 189 distinct values High cardinality
COMUNA is highly correlated with REG_RESHigh correlation
REG_RES is highly correlated with COMUNAHigh correlation
DIA_NAC.1 is highly correlated with DIA_NACHigh correlation
DIA_NAC is highly correlated with DIA_NAC.1High correlation
MES_NAC.1 is highly correlated with MES_NACHigh correlation
MES_NAC is highly correlated with MES_NAC.1High correlation
ANO1 is highly correlated with ANO_DEF and 2 other fieldsHigh correlation
ANO_DEF is highly correlated with ANO1 and 2 other fieldsHigh correlation
ANO2 is highly correlated with ANO_DEF and 2 other fieldsHigh correlation
DIA_DEF.1 is highly correlated with DIA_DEFHigh correlation
DIA_DEF is highly correlated with DIA_DEF.1High correlation
MES_DEF.1 is highly correlated with MES_DEFHigh correlation
MES_DEF is highly correlated with MES_DEF.1High correlation
ANO_DEF.1 is highly correlated with ANO_DEF and 2 other fieldsHigh correlation
difb is highly correlated with difa and 1 other fieldsHigh correlation
difa is highly correlated with difb and 1 other fieldsHigh correlation
difc is highly correlated with difa and 1 other fieldsHigh correlation
mes_suicidio is highly correlated with dist_absoluta and 1 other fieldsHigh correlation
dist_absoluta is highly correlated with mes_suicidio and 1 other fieldsHigh correlation
bimestre is highly correlated with dist_absoluta and 1 other fieldsHigh correlation
RENGOETARIO_N is highly correlated with EDAD_CANT and 2 other fieldsHigh correlation
EDAD_CANT is highly correlated with RENGOETARIO_N and 2 other fieldsHigh correlation
RANGO ETARIO is highly correlated with EDAD_CANT and 2 other fieldsHigh correlation
EDAD_LABORAL is highly correlated with EDAD_CANT and 2 other fieldsHigh correlation
ESTACION is highly correlated with ESTACION NHigh correlation
ESTACION N is highly correlated with ESTACIONHigh correlation
SUBTIPO_MEC is highly correlated with MEC_SUICIDAHigh correlation
MEC_SUICIDA is highly correlated with SUBTIPO_MECHigh correlation
AÑONACIM is highly skewed (γ1 = -33.87992814) Skewed
CURSO_INS has 611 (3.2%) zeros Zeros

Reproduction

Analysis started2020-09-18 15:48:37.738316
Analysis finished2020-09-18 15:50:02.678069
Duration1 minute and 24.94 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

DIA_NAC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.567541
Minimum0
Maximum31
Zeros2
Zeros (%)< 0.1%
Memory size151.1 KiB
2020-09-18T10:50:02.750117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.768529562
Coefficient of variation (CV)0.5632572005
Kurtosis-1.184671108
Mean15.567541
Median Absolute Deviation (MAD)8
Skewness0.0258259228
Sum300905
Variance76.88711068
MonotocityNot monotonic
2020-09-18T10:50:02.860843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
107714.0%
 
17353.8%
 
157293.8%
 
207113.7%
 
56703.5%
 
256683.5%
 
66663.4%
 
86633.4%
 
76493.4%
 
176443.3%
 
186423.3%
 
266353.3%
 
96283.2%
 
166183.2%
 
246173.2%
 
116153.2%
 
226143.2%
 
286093.2%
 
276053.1%
 
26053.1%
 
196053.1%
 
146013.1%
 
126003.1%
 
46003.1%
 
135973.1%
 
Other values (7)323216.7%
 
ValueCountFrequency (%) 
02< 0.1%
 
17353.8%
 
26053.1%
 
35672.9%
 
46003.1%
 
56703.5%
 
66663.4%
 
76493.4%
 
86633.4%
 
96283.2%
 
ValueCountFrequency (%) 
313201.7%
 
305743.0%
 
295923.1%
 
286093.2%
 
276053.1%
 
266353.3%
 
256683.5%
 
246173.2%
 
235923.1%
 
226143.2%
 

MES_NAC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.734699157
Minimum0
Maximum12
Zeros2
Zeros (%)< 0.1%
Memory size151.1 KiB
2020-09-18T10:50:02.961747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.463052509
Coefficient of variation (CV)0.5142104241
Kurtosis-1.206179845
Mean6.734699157
Median Absolute Deviation (MAD)3
Skewness-0.1191701935
Sum130175
Variance11.99273268
MonotocityNot monotonic
2020-09-18T10:50:03.058179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
1018299.5%
 
918099.4%
 
817549.1%
 
1217298.9%
 
1117018.8%
 
116098.3%
 
615658.1%
 
715388.0%
 
314837.7%
 
514777.6%
 
414247.4%
 
214097.3%
 
02< 0.1%
 
ValueCountFrequency (%) 
02< 0.1%
 
116098.3%
 
214097.3%
 
314837.7%
 
414247.4%
 
514777.6%
 
615658.1%
 
715388.0%
 
817549.1%
 
918099.4%
 
ValueCountFrequency (%) 
1217298.9%
 
1117018.8%
 
1018299.5%
 
918099.4%
 
817549.1%
 
715388.0%
 
615658.1%
 
514777.6%
 
414247.4%
 
314837.7%
 

AÑONACIM
Real number (ℝ≥0)

SKEWED

Distinct98
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.855347
Minimum0
Maximum2007
Zeros1
Zeros (%)< 0.1%
Memory size151.1 KiB
2020-09-18T10:50:03.171696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1936
Q11957
median1971
Q31984
95-th percentile1993
Maximum2007
Range2007
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.72503537
Coefficient of variation (CV)0.01154225749
Kurtosis2913.941136
Mean1968.855347
Median Absolute Deviation (MAD)13
Skewness-33.87992814
Sum38056005
Variance516.4272325
MonotocityNot monotonic
2020-09-18T10:50:03.294318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19894482.3%
 
19864482.3%
 
19874452.3%
 
19884382.3%
 
19854192.2%
 
19804092.1%
 
19834092.1%
 
19794082.1%
 
19814022.1%
 
19733982.1%
 
19753962.0%
 
19823932.0%
 
19843892.0%
 
19643862.0%
 
19913852.0%
 
19623832.0%
 
19663812.0%
 
19743812.0%
 
19653782.0%
 
19673782.0%
 
19903711.9%
 
19683651.9%
 
19613651.9%
 
19713621.9%
 
19723501.8%
 
Other values (73)944248.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
19051< 0.1%
 
19071< 0.1%
 
19091< 0.1%
 
19101< 0.1%
 
19124< 0.1%
 
19143< 0.1%
 
19156< 0.1%
 
19166< 0.1%
 
19177< 0.1%
 
ValueCountFrequency (%) 
20071< 0.1%
 
20044< 0.1%
 
2003100.1%
 
2002130.1%
 
2001310.2%
 
2000370.2%
 
1999670.3%
 
1998870.5%
 
19971010.5%
 
19961290.7%
 

ANO1_NAC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
19
19232 
20
 
96
0
 
1
ValueCountFrequency (%) 
191923299.5%
 
20960.5%
 
01< 0.1%
 
2020-09-18T10:50:03.424414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-18T10:50:03.501161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:03.584451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.999948264
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11923249.8%
 
91923249.8%
 
0970.3%
 
2960.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number38657100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11923249.8%
 
91923249.8%
 
0970.3%
 
2960.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common38657100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11923249.8%
 
91923249.8%
 
0970.3%
 
2960.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII38657100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11923249.8%
 
91923249.8%
 
0970.3%
 
2960.2%
 

ANO2_NAC
Real number (ℝ≥0)

Distinct96
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.45698174
Minimum0
Maximum99
Zeros38
Zeros (%)0.2%
Memory size151.1 KiB
2020-09-18T10:50:03.697726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q157
median71
Q383
95-th percentile93
Maximum99
Range99
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.26328907
Coefficient of variation (CV)0.2667849006
Kurtosis0.1409362284
Mean68.45698174
Median Absolute Deviation (MAD)13
Skewness-0.6611727932
Sum1323205
Variance333.5477277
MonotocityNot monotonic
2020-09-18T10:50:03.820192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
894482.3%
 
864482.3%
 
874452.3%
 
884382.3%
 
854192.2%
 
834092.1%
 
804092.1%
 
794082.1%
 
814022.1%
 
733982.1%
 
753962.0%
 
823932.0%
 
843892.0%
 
643862.0%
 
913852.0%
 
623832.0%
 
663812.0%
 
743812.0%
 
653782.0%
 
673782.0%
 
903711.9%
 
683651.9%
 
613651.9%
 
713621.9%
 
723501.8%
 
Other values (71)944248.8%
 
ValueCountFrequency (%) 
0380.2%
 
1310.2%
 
2130.1%
 
3100.1%
 
44< 0.1%
 
51< 0.1%
 
72< 0.1%
 
91< 0.1%
 
101< 0.1%
 
124< 0.1%
 
ValueCountFrequency (%) 
99670.3%
 
98870.5%
 
971010.5%
 
961290.7%
 
952071.1%
 
942331.2%
 
932771.4%
 
923421.8%
 
913852.0%
 
903711.9%
 

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
1
15705 
2
3624 
ValueCountFrequency (%) 
11570581.3%
 
2362418.7%
 
2020-09-18T10:50:03.933666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:03.996350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:04.066286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11570581.3%
 
2362418.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11570581.3%
 
2362418.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11570581.3%
 
2362418.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11570581.3%
 
2362418.7%
 

EST_CIVIL
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
1
10503 
2
7984 
3
 
393
9
 
321
4
 
125
Other values (2)
 
3
ValueCountFrequency (%) 
11050354.3%
 
2798441.3%
 
33932.0%
 
93211.7%
 
41250.6%
 
52< 0.1%
 
61< 0.1%
 
2020-09-18T10:50:04.162565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-18T10:50:04.228204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:04.328705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11050354.3%
 
2798441.3%
 
33932.0%
 
93211.7%
 
41250.6%
 
52< 0.1%
 
61< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11050354.3%
 
2798441.3%
 
33932.0%
 
93211.7%
 
41250.6%
 
52< 0.1%
 
61< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11050354.3%
 
2798441.3%
 
33932.0%
 
93211.7%
 
41250.6%
 
52< 0.1%
 
61< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11050354.3%
 
2798441.3%
 
33932.0%
 
93211.7%
 
41250.6%
 
52< 0.1%
 
61< 0.1%
 

EDAD_TIPO
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
1
19329 
ValueCountFrequency (%) 
119329100.0%
 
2020-09-18T10:50:04.423658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:04.483195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:04.545362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters1
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
119329100.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
119329100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
119329100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
119329100.0%
 

EDAD_CANT
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct96
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
23
 
479
29
 
435
22
 
434
24
 
431
21
 
423
Other values (91)
17127 
ValueCountFrequency (%) 
234792.5%
 
294352.3%
 
224342.2%
 
244312.2%
 
214232.2%
 
284162.2%
 
274122.1%
 
253992.1%
 
263982.1%
 
473972.1%
 
413922.0%
 
193852.0%
 
383842.0%
 
303832.0%
 
203802.0%
 
353782.0%
 
313772.0%
 
463761.9%
 
433741.9%
 
343711.9%
 
333631.9%
 
443631.9%
 
363611.9%
 
403601.9%
 
453581.9%
 
Other values (71)950049.1%
 
2020-09-18T10:50:04.670214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)< 0.1%
2020-09-18T10:50:04.791574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2611415.8%
 
3556714.4%
 
4553014.3%
 
5480512.4%
 
636559.5%
 
135699.2%
 
730087.8%
 
824276.3%
 
920335.3%
 
019505.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number38658100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2611415.8%
 
3556714.4%
 
4553014.3%
 
5480512.4%
 
636559.5%
 
135699.2%
 
730087.8%
 
824276.3%
 
920335.3%
 
019505.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common38658100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2611415.8%
 
3556714.4%
 
4553014.3%
 
5480512.4%
 
636559.5%
 
135699.2%
 
730087.8%
 
824276.3%
 
920335.3%
 
019505.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII38658100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2611415.8%
 
3556714.4%
 
4553014.3%
 
5480512.4%
 
636559.5%
 
135699.2%
 
730087.8%
 
824276.3%
 
920335.3%
 
019505.0%
 

RENGOETARIO_N
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
3
8215 
4
7291 
5
2209 
2
1545 
1
 
69
ValueCountFrequency (%) 
3821542.5%
 
4729137.7%
 
5220911.4%
 
215458.0%
 
1690.4%
 
2020-09-18T10:50:04.909584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:04.985162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:05.075029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3821542.5%
 
4729137.7%
 
5220911.4%
 
215458.0%
 
1690.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3821542.5%
 
4729137.7%
 
5220911.4%
 
215458.0%
 
1690.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3821542.5%
 
4729137.7%
 
5220911.4%
 
215458.0%
 
1690.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3821542.5%
 
4729137.7%
 
5220911.4%
 
215458.0%
 
1690.4%
 

RANGO ETARIO
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
adulto joven
8215 
adulto
7291 
adulto mayor
2209 
Adolescente
1545 
niño(a)
 
69
ValueCountFrequency (%) 
adulto joven821542.5%
 
adulto729137.7%
 
adulto mayor220911.4%
 
Adolescente15458.0%
 
niño(a)690.4%
 
2020-09-18T10:50:05.183080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:05.259723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:05.357441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length12
Mean length9.638988049
Min length6

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o2975316.0%
 
a1999310.7%
 
d1926010.3%
 
l1926010.3%
 
t1926010.3%
 
u177159.5%
 
e128506.9%
 
104245.6%
 
n98295.3%
 
j82154.4%
 
v82154.4%
 
m22091.2%
 
y22091.2%
 
r22091.2%
 
A15450.8%
 
s15450.8%
 
c15450.8%
 
i69< 0.1%
 
ñ69< 0.1%
 
(69< 0.1%
 
)69< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter17420593.5%
 
Space Separator104245.6%
 
Uppercase Letter15450.8%
 
Open Punctuation69< 0.1%
 
Close Punctuation69< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o2975317.1%
 
a1999311.5%
 
d1926011.1%
 
l1926011.1%
 
t1926011.1%
 
u1771510.2%
 
e128507.4%
 
n98295.6%
 
j82154.7%
 
v82154.7%
 
m22091.3%
 
y22091.3%
 
r22091.3%
 
s15450.9%
 
c15450.9%
 
i69< 0.1%
 
ñ69< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10424100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(69100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)69100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1545100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin17575094.3%
 
Common105625.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o2975316.9%
 
a1999311.4%
 
d1926011.0%
 
l1926011.0%
 
t1926011.0%
 
u1771510.1%
 
e128507.3%
 
n98295.6%
 
j82154.7%
 
v82154.7%
 
m22091.3%
 
y22091.3%
 
r22091.3%
 
A15450.9%
 
s15450.9%
 
c15450.9%
 
i69< 0.1%
 
ñ69< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
1042498.7%
 
(690.7%
 
)690.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII186243> 99.9%
 
None69< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o2975316.0%
 
a1999310.7%
 
d1926010.3%
 
l1926010.3%
 
t1926010.3%
 
u177159.5%
 
e128506.9%
 
104245.6%
 
n98295.3%
 
j82154.4%
 
v82154.4%
 
m22091.2%
 
y22091.2%
 
r22091.2%
 
A15450.8%
 
s15450.8%
 
c15450.8%
 
i69< 0.1%
 
(69< 0.1%
 
)69< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ69100.0%
 

EDAD_LABORAL
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
EdadLab_MED
8215 
EdadLab_MAX
7291 
Edad_AVAN
2209 
EdadLab_TEM
1545 
niño(a)
 
69
ValueCountFrequency (%) 
EdadLab_MED821542.5%
 
EdadLab_MAX729137.7%
 
Edad_AVAN220911.4%
 
EdadLab_TEM15458.0%
 
niño(a)690.4%
 
2020-09-18T10:50:05.469895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:05.545470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:05.652484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.75715247
Min length7

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
d3852018.5%
 
a3638017.5%
 
E2902014.0%
 
_192609.3%
 
L170518.2%
 
b170518.2%
 
M170518.2%
 
A117095.6%
 
D82154.0%
 
X72913.5%
 
V22091.1%
 
N22091.1%
 
T15450.7%
 
n69< 0.1%
 
i69< 0.1%
 
ñ69< 0.1%
 
o69< 0.1%
 
(69< 0.1%
 
)69< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter9630046.3%
 
Lowercase Letter9222744.4%
 
Connector Punctuation192609.3%
 
Open Punctuation69< 0.1%
 
Close Punctuation69< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E2902030.1%
 
L1705117.7%
 
M1705117.7%
 
A1170912.2%
 
D82158.5%
 
X72917.6%
 
V22092.3%
 
N22092.3%
 
T15451.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d3852041.8%
 
a3638039.4%
 
b1705118.5%
 
n690.1%
 
i690.1%
 
ñ690.1%
 
o690.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_19260100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(69100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)69100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin18852790.7%
 
Common193989.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d3852020.4%
 
a3638019.3%
 
E2902015.4%
 
L170519.0%
 
b170519.0%
 
M170519.0%
 
A117096.2%
 
D82154.4%
 
X72913.9%
 
V22091.2%
 
N22091.2%
 
T15450.8%
 
n69< 0.1%
 
i69< 0.1%
 
ñ69< 0.1%
 
o69< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_1926099.3%
 
(690.4%
 
)690.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII207856> 99.9%
 
None69< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
d3852018.5%
 
a3638017.5%
 
E2902014.0%
 
_192609.3%
 
L170518.2%
 
b170518.2%
 
M170518.2%
 
A117095.6%
 
D82154.0%
 
X72913.5%
 
V22091.1%
 
N22091.1%
 
T15450.7%
 
n69< 0.1%
 
i69< 0.1%
 
o69< 0.1%
 
(69< 0.1%
 
)69< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ69100.0%
 

CURSO_INS
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.356303999
Minimum0
Maximum9
Zeros611
Zeros (%)3.2%
Memory size151.1 KiB
2020-09-18T10:50:05.758901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.103183864
Coefficient of variation (CV)0.4827908852
Kurtosis-0.4841706321
Mean4.356303999
Median Absolute Deviation (MAD)1
Skewness0.1847114878
Sum84203
Variance4.423382365
MonotocityNot monotonic
2020-09-18T10:50:05.841295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4716737.1%
 
8275814.3%
 
6229311.9%
 
2219911.4%
 
316048.3%
 
512656.5%
 
19815.1%
 
06113.2%
 
74312.2%
 
9200.1%
 
ValueCountFrequency (%) 
06113.2%
 
19815.1%
 
2219911.4%
 
316048.3%
 
4716737.1%
 
512656.5%
 
6229311.9%
 
74312.2%
 
8275814.3%
 
9200.1%
 
ValueCountFrequency (%) 
9200.1%
 
8275814.3%
 
74312.2%
 
6229311.9%
 
512656.5%
 
4716737.1%
 
316048.3%
 
2219911.4%
 
19815.1%
 
06113.2%
 

NIVEL_INS
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.804128512
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:05.924774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.158318928
Coefficient of variation (CV)0.4130762636
Kurtosis-1.018823427
Mean2.804128512
Median Absolute Deviation (MAD)1
Skewness0.1753799955
Sum54201
Variance1.341702739
MonotocityNot monotonic
2020-09-18T10:50:06.008310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
2842143.6%
 
4738538.2%
 
1201610.4%
 
38864.6%
 
56113.2%
 
9100.1%
 
ValueCountFrequency (%) 
1201610.4%
 
2842143.6%
 
38864.6%
 
4738538.2%
 
56113.2%
 
9100.1%
 
ValueCountFrequency (%) 
9100.1%
 
56113.2%
 
4738538.2%
 
38864.6%
 
2842143.6%
 
1201610.4%
 

ACTIVIDAD
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
1
11578 
0
6392 
2
1325 
9
 
34
ValueCountFrequency (%) 
11157859.9%
 
0639233.1%
 
213256.9%
 
9340.2%
 
2020-09-18T10:50:06.115811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:06.185199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:06.266695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11157859.9%
 
0639233.1%
 
213256.9%
 
9340.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11157859.9%
 
0639233.1%
 
213256.9%
 
9340.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11157859.9%
 
0639233.1%
 
213256.9%
 
9340.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11157859.9%
 
0639233.1%
 
213256.9%
 
9340.2%
 

OCUPACION
Categorical

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
5
2455 
5
2409 
7
1340 
7
1251 
9
1245 
Other values (17)
10629 
ValueCountFrequency (%) 
5245512.7%
 
5240912.5%
 
713406.9%
 
712516.5%
 
912456.4%
 
X11706.1%
 
911566.0%
 
211215.8%
 
610345.3%
 
310215.3%
 
29665.0%
 
38574.4%
 
67243.7%
 
85823.0%
 
85642.9%
 
x4312.2%
 
43962.0%
 
43701.9%
 
1810.4%
 
1650.3%
 
0490.3%
 
0420.2%
 
2020-09-18T10:50:06.374926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:06.478749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
5486425.2%
 
7259113.4%
 
9240112.4%
 
2208710.8%
 
318789.7%
 
617589.1%
 
X11706.1%
 
811465.9%
 
47664.0%
 
x4312.2%
 
11460.8%
 
0910.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1772891.7%
 
Uppercase Letter11706.1%
 
Lowercase Letter4312.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5486427.4%
 
7259114.6%
 
9240113.5%
 
2208711.8%
 
3187810.6%
 
617589.9%
 
811466.5%
 
47664.3%
 
11460.8%
 
0910.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
X1170100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
x431100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1772891.7%
 
Latin16018.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5486427.4%
 
7259114.6%
 
9240113.5%
 
2208711.8%
 
3187810.6%
 
617589.9%
 
811466.5%
 
47664.3%
 
11460.8%
 
0910.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
X117073.1%
 
x43126.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
5486425.2%
 
7259113.4%
 
9240112.4%
 
2208710.8%
 
318789.7%
 
617589.1%
 
X11706.1%
 
811465.9%
 
47664.0%
 
x4312.2%
 
11460.8%
 
0910.5%
 

CATEGORIA
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
0
6392 
3
5262 
2
3562 
4
2642 
9
1359 
ValueCountFrequency (%) 
0639233.1%
 
3526227.2%
 
2356218.4%
 
4264213.7%
 
913597.0%
 
11120.6%
 
2020-09-18T10:50:06.584815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:06.655789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:06.753774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0639233.1%
 
3526227.2%
 
2356218.4%
 
4264213.7%
 
913597.0%
 
11120.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0639233.1%
 
3526227.2%
 
2356218.4%
 
4264213.7%
 
913597.0%
 
11120.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0639233.1%
 
3526227.2%
 
2356218.4%
 
4264213.7%
 
913597.0%
 
11120.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0639233.1%
 
3526227.2%
 
2356218.4%
 
4264213.7%
 
913597.0%
 
11120.6%
 
Distinct3634
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Minimum2007-01-01 00:00:00
Maximum2016-12-31 00:00:00
2020-09-18T10:50:06.860377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:06.973528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ESTACION N
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
1
9339 
3
3500 
4
3379 
2
3111 
ValueCountFrequency (%) 
1933948.3%
 
3350018.1%
 
4337917.5%
 
2311116.1%
 
2020-09-18T10:50:07.094874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:07.162237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:07.244212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1933948.3%
 
3350018.1%
 
4337917.5%
 
2311116.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1933948.3%
 
3350018.1%
 
4337917.5%
 
2311116.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1933948.3%
 
3350018.1%
 
4337917.5%
 
2311116.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1933948.3%
 
3350018.1%
 
4337917.5%
 
2311116.1%
 

ESTACION
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
VERANO
9339 
PRIMAVERA
3500 
INVIERNO
3379 
OTOÑO
3111 
ValueCountFrequency (%) 
VERANO933948.3%
 
PRIMAVERA350018.1%
 
INVIERNO337917.5%
 
OTOÑO311116.1%
 
2020-09-18T10:50:07.344299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:07.411036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:07.499014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.731905427
Min length5

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O2205116.9%
 
R1971815.2%
 
A1633912.6%
 
V1621812.5%
 
E1621812.5%
 
N1609712.4%
 
I102587.9%
 
P35002.7%
 
M35002.7%
 
T31112.4%
 
Ñ31112.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter130121100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O2205116.9%
 
R1971815.2%
 
A1633912.6%
 
V1621812.5%
 
E1621812.5%
 
N1609712.4%
 
I102587.9%
 
P35002.7%
 
M35002.7%
 
T31112.4%
 
Ñ31112.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin130121100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O2205116.9%
 
R1971815.2%
 
A1633912.6%
 
V1621812.5%
 
E1621812.5%
 
N1609712.4%
 
I102587.9%
 
P35002.7%
 
M35002.7%
 
T31112.4%
 
Ñ31112.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12701097.6%
 
None31112.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O2205117.4%
 
R1971815.5%
 
A1633912.9%
 
V1621812.8%
 
E1621812.8%
 
N1609712.7%
 
I102588.1%
 
P35002.8%
 
M35002.8%
 
T31112.4%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ3111100.0%
 

DIA_SEMANA
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
domingo
3464 
sábado
2897 
lunes
2882 
martes
2580 
viernes
2549 
Other values (2)
4957 
ValueCountFrequency (%) 
domingo346417.9%
 
sábado289715.0%
 
lunes288214.9%
 
martes258013.3%
 
viernes254913.2%
 
miércoles248012.8%
 
jueves247712.8%
 
2020-09-18T10:50:07.604123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:07.673557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:07.789092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.546898443
Min length5

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1799414.2%
 
s1586512.5%
 
o123059.7%
 
n88957.0%
 
m85246.7%
 
i84936.7%
 
r76096.0%
 
d63615.0%
 
a54774.3%
 
l53624.2%
 
u53594.2%
 
v50264.0%
 
g34642.7%
 
á28972.3%
 
b28972.3%
 
t25802.0%
 
é24802.0%
 
c24802.0%
 
j24772.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter126545100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1799414.2%
 
s1586512.5%
 
o123059.7%
 
n88957.0%
 
m85246.7%
 
i84936.7%
 
r76096.0%
 
d63615.0%
 
a54774.3%
 
l53624.2%
 
u53594.2%
 
v50264.0%
 
g34642.7%
 
á28972.3%
 
b28972.3%
 
t25802.0%
 
é24802.0%
 
c24802.0%
 
j24772.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin126545100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1799414.2%
 
s1586512.5%
 
o123059.7%
 
n88957.0%
 
m85246.7%
 
i84936.7%
 
r76096.0%
 
d63615.0%
 
a54774.3%
 
l53624.2%
 
u53594.2%
 
v50264.0%
 
g34642.7%
 
á28972.3%
 
b28972.3%
 
t25802.0%
 
é24802.0%
 
c24802.0%
 
j24772.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12116895.8%
 
None53774.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1799414.9%
 
s1586513.1%
 
o1230510.2%
 
n88957.3%
 
m85247.0%
 
i84937.0%
 
r76096.3%
 
d63615.2%
 
a54774.5%
 
l53624.4%
 
u53594.4%
 
v50264.1%
 
g34642.9%
 
b28972.4%
 
t25802.1%
 
c24802.0%
 
j24772.0%
 

Most frequent None characters

ValueCountFrequency (%) 
á289753.9%
 
é248046.1%
 

DIA_DEF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.459672
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:07.896407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.852605637
Coefficient of variation (CV)0.572625709
Kurtosis-1.204810612
Mean15.459672
Median Absolute Deviation (MAD)8
Skewness0.0260246801
Sum298820
Variance78.36862657
MonotocityNot monotonic
2020-09-18T10:50:07.999757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
17213.7%
 
37003.6%
 
26973.6%
 
196783.5%
 
66663.4%
 
246553.4%
 
96543.4%
 
116533.4%
 
266533.4%
 
56473.3%
 
226473.3%
 
166423.3%
 
76403.3%
 
46383.3%
 
186373.3%
 
216373.3%
 
86363.3%
 
256363.3%
 
206323.3%
 
106163.2%
 
236143.2%
 
146113.2%
 
136093.2%
 
176033.1%
 
155893.0%
 
Other values (6)321816.6%
 
ValueCountFrequency (%) 
17213.7%
 
26973.6%
 
37003.6%
 
46383.3%
 
56473.3%
 
66663.4%
 
76403.3%
 
86363.3%
 
96543.4%
 
106163.2%
 
ValueCountFrequency (%) 
313711.9%
 
305672.9%
 
295312.7%
 
285793.0%
 
275813.0%
 
266533.4%
 
256363.3%
 
246553.4%
 
236143.2%
 
226473.3%
 

MES_DEF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.598685912
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:08.106344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.554361468
Coefficient of variation (CV)0.5386468631
Kurtosis-1.273037101
Mean6.598685912
Median Absolute Deviation (MAD)3
Skewness-0.06200530247
Sum127546
Variance12.63348544
MonotocityNot monotonic
2020-09-18T10:50:08.194879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
118979.8%
 
1217959.3%
 
1017409.0%
 
917288.9%
 
1117238.9%
 
315928.2%
 
815658.1%
 
515398.0%
 
414567.5%
 
214517.5%
 
714297.4%
 
614147.3%
 
ValueCountFrequency (%) 
118979.8%
 
214517.5%
 
315928.2%
 
414567.5%
 
515398.0%
 
614147.3%
 
714297.4%
 
815658.1%
 
917288.9%
 
1017409.0%
 
ValueCountFrequency (%) 
1217959.3%
 
1117238.9%
 
1017409.0%
 
917288.9%
 
815658.1%
 
714297.4%
 
614147.3%
 
515398.0%
 
414567.5%
 
315928.2%
 

ANO_DEF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.352579
Minimum2007
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:08.291636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12009
median2011
Q32014
95-th percentile2016
Maximum2016
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.870012355
Coefficient of variation (CV)0.001426906642
Kurtosis-1.223166562
Mean2011.352579
Median Absolute Deviation (MAD)2
Skewness0.09535227155
Sum38877434
Variance8.23697092
MonotocityIncreasing
2020-09-18T10:50:08.372917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2008216611.2%
 
2009214811.1%
 
2011202710.5%
 
2010200110.4%
 
2007192610.0%
 
201618589.6%
 
201218419.5%
 
201518359.5%
 
201417899.3%
 
201317389.0%
 
ValueCountFrequency (%) 
2007192610.0%
 
2008216611.2%
 
2009214811.1%
 
2010200110.4%
 
2011202710.5%
 
201218419.5%
 
201317389.0%
 
201417899.3%
 
201518359.5%
 
201618589.6%
 
ValueCountFrequency (%) 
201618589.6%
 
201518359.5%
 
201417899.3%
 
201317389.0%
 
201218419.5%
 
2011202710.5%
 
2010200110.4%
 
2009214811.1%
 
2008216611.2%
 
2007192610.0%
 

LUGAR_DEF
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
3
10008 
2
6788 
1
2533 
ValueCountFrequency (%) 
31000851.8%
 
2678835.1%
 
1253313.1%
 
2020-09-18T10:50:08.469838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:08.539005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:08.617507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
31000851.8%
 
2678835.1%
 
1253313.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
31000851.8%
 
2678835.1%
 
1253313.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
31000851.8%
 
2678835.1%
 
1253313.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
31000851.8%
 
2678835.1%
 
1253313.1%
 

REG_RES
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.277769155
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:08.707575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q313
95-th percentile13
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.65112232
Coefficient of variation (CV)0.393534508
Kurtosis-1.094995514
Mean9.277769155
Median Absolute Deviation (MAD)4
Skewness-0.3424299944
Sum179330
Variance13.3306942
MonotocityNot monotonic
2020-09-18T10:50:08.795006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
13689235.7%
 
8241912.5%
 
5194710.1%
 
913176.8%
 
712976.7%
 
1012616.5%
 
611505.9%
 
48074.2%
 
145452.8%
 
25372.8%
 
33401.8%
 
12671.4%
 
122081.1%
 
151820.9%
 
111600.8%
 
ValueCountFrequency (%) 
12671.4%
 
25372.8%
 
33401.8%
 
48074.2%
 
5194710.1%
 
611505.9%
 
712976.7%
 
8241912.5%
 
913176.8%
 
1012616.5%
 
ValueCountFrequency (%) 
151820.9%
 
145452.8%
 
13689235.7%
 
122081.1%
 
111600.8%
 
1012616.5%
 
913176.8%
 
8241912.5%
 
712976.7%
 
611505.9%
 

SERV_RES
Real number (ℝ≥0)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.99358477
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:08.899718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median14
Q318
95-th percentile26
Maximum33
Range32
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.849717225
Coefficient of variation (CV)0.4894898154
Kurtosis-0.1394890655
Mean13.99358477
Median Absolute Deviation (MAD)5
Skewness0.4487015366
Sum270482
Variance46.91862606
MonotocityNot monotonic
2020-09-18T10:50:09.003574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
1014257.4%
 
1413887.2%
 
1612976.7%
 
1312666.5%
 
1511505.9%
 
910505.4%
 
219685.0%
 
129344.8%
 
79264.8%
 
118374.3%
 
58074.2%
 
177083.7%
 
66593.4%
 
245823.0%
 
185542.9%
 
225452.8%
 
35372.8%
 
205202.7%
 
233842.0%
 
83541.8%
 
293491.8%
 
193491.8%
 
43401.8%
 
332951.5%
 
282881.5%
 
Other values (4)8174.2%
 
ValueCountFrequency (%) 
11820.9%
 
22671.4%
 
35372.8%
 
43401.8%
 
58074.2%
 
66593.4%
 
79264.8%
 
83541.8%
 
910505.4%
 
1014257.4%
 
ValueCountFrequency (%) 
332951.5%
 
293491.8%
 
282881.5%
 
262081.1%
 
251600.8%
 
245823.0%
 
233842.0%
 
225452.8%
 
219685.0%
 
205202.7%
 

COMUNA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct350
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9460.871023
Minimum1101
Maximum15202
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:09.123713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile3101
Q16301
median9119
Q313117
95-th percentile13502
Maximum15202
Range14101
Interquartile range (IQR)6816

Descriptive statistics

Standard deviation3622.227515
Coefficient of variation (CV)0.3828640626
Kurtosis-1.035603096
Mean9460.871023
Median Absolute Deviation (MAD)3985
Skewness-0.3603792783
Sum182869176
Variance13120532.17
MonotocityNot monotonic
2020-09-18T10:50:09.243488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132015142.7%
 
131193952.0%
 
51013721.9%
 
61013411.8%
 
131103401.8%
 
101013191.7%
 
134012881.5%
 
91012851.5%
 
103012701.4%
 
131122681.4%
 
51092651.4%
 
21012581.3%
 
131242541.3%
 
71012461.3%
 
131012421.3%
 
131222321.2%
 
22012291.2%
 
41012261.2%
 
84012251.2%
 
131052241.2%
 
83012211.1%
 
131282111.1%
 
131272051.1%
 
41021951.0%
 
81011941.0%
 
Other values (325)1251064.7%
 
ValueCountFrequency (%) 
11011790.9%
 
11041< 0.1%
 
11062< 0.1%
 
1107730.4%
 
1201160.1%
 
14017< 0.1%
 
14022< 0.1%
 
14041< 0.1%
 
14052< 0.1%
 
21012581.3%
 
ValueCountFrequency (%) 
152021< 0.1%
 
151011650.9%
 
14204440.2%
 
14203210.1%
 
14202200.1%
 
14201530.3%
 
14108560.3%
 
14107260.1%
 
14106240.1%
 
14105160.1%
 

DESC_COMUNA
Categorical

HIGH CARDINALITY

Distinct221
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
SANTIAGO
5088 
INDEPENDENCIA
2067 
ISLA DE PASCUA
 
807
LONQUIMAY
 
408
RANCAGUA
 
341
Other values (216)
10618 
ValueCountFrequency (%) 
SANTIAGO508826.3%
 
INDEPENDENCIA206710.7%
 
ISLA DE PASCUA8074.2%
 
LONQUIMAY4082.1%
 
RANCAGUA3411.8%
 
VALDIVIA3191.7%
 
ANGOL2851.5%
 
PUERTO MONTT2701.4%
 
TOCOPILLA2681.4%
 
CURICO2461.3%
 
RINCONADA2431.3%
 
ANTOFAGASTA2291.2%
 
LA SERENA2261.2%
 
LOS ANGELES2251.2%
 
ARAUCO2211.1%
 
CHANCO2111.1%
 
LA HIGUERA1951.0%
 
CHILLAN1941.0%
 
ALTO BIOBIO1911.0%
 
ARICA1800.9%
 
NATALES1800.9%
 
SAN FELIPE1760.9%
 
LINARES1750.9%
 
CHANARAL1670.9%
 
ALHUE1650.9%
 
Other values (196)625232.3%
 
2020-09-18T10:50:09.388362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-09-18T10:50:09.506605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length8
Mean length8.947746909
Min length4

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A2945417.0%
 
N1922711.1%
 
I169759.8%
 
E136467.9%
 
O133257.7%
 
S104226.0%
 
T93195.4%
 
L91035.3%
 
C85705.0%
 
G69014.0%
 
D63383.7%
 
U59633.4%
 
P50152.9%
 
49042.8%
 
R48012.8%
 
M20181.2%
 
H19451.1%
 
Q13690.8%
 
V12350.7%
 
B8460.5%
 
F7170.4%
 
Y5670.3%
 
J1230.1%
 
Z900.1%
 
-78< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter16796997.1%
 
Space Separator49042.8%
 
Dash Punctuation78< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2945417.5%
 
N1922711.4%
 
I1697510.1%
 
E136468.1%
 
O133257.9%
 
S104226.2%
 
T93195.5%
 
L91035.4%
 
C85705.1%
 
G69014.1%
 
D63383.8%
 
U59633.6%
 
P50153.0%
 
R48012.9%
 
M20181.2%
 
H19451.2%
 
Q13690.8%
 
V12350.7%
 
B8460.5%
 
F7170.4%
 
Y5670.3%
 
J1230.1%
 
Z900.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4904100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-78100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin16796997.1%
 
Common49822.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2945417.5%
 
N1922711.4%
 
I1697510.1%
 
E136468.1%
 
O133257.9%
 
S104226.2%
 
T93195.5%
 
L91035.4%
 
C85705.1%
 
G69014.1%
 
D63383.8%
 
U59633.6%
 
P50153.0%
 
R48012.9%
 
M20181.2%
 
H19451.2%
 
Q13690.8%
 
V12350.7%
 
B8460.5%
 
F7170.4%
 
Y5670.3%
 
J1230.1%
 
Z900.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
490498.4%
 
-781.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII172951100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A2945417.0%
 
N1922711.1%
 
I169759.8%
 
E136467.9%
 
O133257.7%
 
S104226.0%
 
T93195.4%
 
L91035.3%
 
C85705.0%
 
G69014.0%
 
D63383.7%
 
U59633.4%
 
P50152.9%
 
49042.8%
 
R48012.8%
 
M20181.2%
 
H19451.1%
 
Q13690.8%
 
V12350.7%
 
B8460.5%
 
F7170.4%
 
Y5670.3%
 
J1230.1%
 
Z900.1%
 
-78< 0.1%
 

URB_RURAL
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
1
15828 
2
3501 
ValueCountFrequency (%) 
11582881.9%
 
2350118.1%
 
2020-09-18T10:50:09.605327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:09.673228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:09.744895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11582881.9%
 
2350118.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11582881.9%
 
2350118.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11582881.9%
 
2350118.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11582881.9%
 
2350118.1%
 

DIAG1
Categorical

HIGH CARDINALITY

Distinct180
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
T71X
16210 
S062
 
710
T07X
 
302
T509
 
195
S069
 
192
Other values (175)
1720 
ValueCountFrequency (%) 
T71X1621083.9%
 
S0627103.7%
 
T07X3021.6%
 
T5091951.0%
 
S0691921.0%
 
T6591380.7%
 
T0681120.6%
 
S0181080.6%
 
T7511000.5%
 
T609910.5%
 
S219890.5%
 
T58X730.4%
 
T430500.3%
 
S119450.2%
 
T424450.2%
 
T149410.2%
 
T600410.2%
 
T549380.2%
 
S299380.2%
 
T542320.2%
 
S311300.2%
 
T011280.1%
 
T065250.1%
 
T603250.1%
 
S269230.1%
 
Other values (155)5482.8%
 
2020-09-18T10:50:09.875368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique69 ?
Unique (%)0.4%
2020-09-18T10:50:09.999218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T1789723.1%
 
11705122.1%
 
71669721.6%
 
X1658721.5%
 
021422.8%
 
614541.9%
 
S14321.9%
 
910891.4%
 
210771.4%
 
57561.0%
 
44050.5%
 
84040.5%
 
33250.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number4140053.5%
 
Uppercase Letter3591646.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T1789749.8%
 
X1658746.2%
 
S14324.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11705141.2%
 
71669740.3%
 
021425.2%
 
614543.5%
 
910892.6%
 
210772.6%
 
57561.8%
 
44051.0%
 
84041.0%
 
33250.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4140053.5%
 
Latin3591646.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T1789749.8%
 
X1658746.2%
 
S14324.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11705141.2%
 
71669740.3%
 
021425.2%
 
614543.5%
 
910892.6%
 
210772.6%
 
57561.8%
 
44051.0%
 
84041.0%
 
33250.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII77316100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T1789723.1%
 
11705122.1%
 
71669721.6%
 
X1658721.5%
 
021422.8%
 
614541.9%
 
S14321.9%
 
910891.4%
 
210771.4%
 
57561.0%
 
44050.5%
 
84040.5%
 
33250.4%
 

DIAG2
Categorical

HIGH CARDINALITY

Distinct189
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
X700
9931 
X708
2046 
X709
1748 
X700
1391 
X740
 
622
Other values (184)
3591 
ValueCountFrequency (%) 
X700993151.4%
 
X708204610.6%
 
X70917489.0%
 
X700 13917.2%
 
X7406223.2%
 
X7045282.7%
 
X8001630.8%
 
X6901530.8%
 
X708 1500.8%
 
X6401280.7%
 
X7481230.6%
 
X7061180.6%
 
X680950.5%
 
X610940.5%
 
X749940.5%
 
X780940.5%
 
X709 850.4%
 
X744830.4%
 
X730800.4%
 
X718790.4%
 
X849760.4%
 
X740 700.4%
 
X670600.3%
 
X649590.3%
 
X704 590.3%
 
Other values (164)12006.2%
 
2020-09-18T10:50:10.124638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique56 ?
Unique (%)0.3%
2020-09-18T10:50:10.961973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length4.104868333
Min length4

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
02968037.4%
 
X1932924.4%
 
71790822.6%
 
836234.6%
 
925703.2%
 
423332.9%
 
20272.6%
 
612201.5%
 
13170.4%
 
31350.2%
 
21170.1%
 
5840.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5798773.1%
 
Uppercase Letter1932924.4%
 
Space Separator20272.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
X19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
02968051.2%
 
71790830.9%
 
836236.2%
 
925704.4%
 
423334.0%
 
612202.1%
 
13170.5%
 
31350.2%
 
21170.2%
 
5840.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2027100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6001475.6%
 
Latin1932924.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
X19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
02968049.5%
 
71790829.8%
 
836236.0%
 
925704.3%
 
423333.9%
 
20273.4%
 
612202.0%
 
13170.5%
 
31350.2%
 
21170.2%
 
5840.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII79343100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
02968037.4%
 
X1932924.4%
 
71790822.6%
 
836234.6%
 
925703.2%
 
423332.9%
 
20272.6%
 
612201.5%
 
13170.4%
 
31350.2%
 
21170.1%
 
5840.1%
 

MEC_SUICIDA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Lesión
18404 
Envenenamiento
 
925
ValueCountFrequency (%) 
Lesión1840495.2%
 
Envenenamiento9254.8%
 
2020-09-18T10:50:11.061559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:11.125733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:11.205318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length6
Mean length6.382844431
Min length6

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2210417.9%
 
e2117917.2%
 
i1932915.7%
 
L1840414.9%
 
s1840414.9%
 
ó1840414.9%
 
E9250.7%
 
v9250.7%
 
a9250.7%
 
m9250.7%
 
t9250.7%
 
o9250.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10404584.3%
 
Uppercase Letter1932915.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L1840495.2%
 
E9254.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2210421.2%
 
e2117920.4%
 
i1932918.6%
 
s1840417.7%
 
ó1840417.7%
 
v9250.9%
 
a9250.9%
 
m9250.9%
 
t9250.9%
 
o9250.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin123374100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2210417.9%
 
e2117917.2%
 
i1932915.7%
 
L1840414.9%
 
s1840414.9%
 
ó1840414.9%
 
E9250.7%
 
v9250.7%
 
a9250.7%
 
m9250.7%
 
t9250.7%
 
o9250.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10497085.1%
 
None1840414.9%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2210421.1%
 
e2117920.2%
 
i1932918.4%
 
L1840417.5%
 
s1840417.5%
 
E9250.9%
 
v9250.9%
 
a9250.9%
 
m9250.9%
 
t9250.9%
 
o9250.9%
 

Most frequent None characters

ValueCountFrequency (%) 
ó18404100.0%
 

SUBTIPO_MEC
Categorical

HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Ahorcamiento
16312 
Arma de Fuego
 
1187
Precipitación de Altura
 
370
Otros Químicos
 
260
Otras Drogas
 
241
Other values (16)
 
959
ValueCountFrequency (%) 
Ahorcamiento1631284.4%
 
Arma de Fuego11876.1%
 
Precipitación de Altura3701.9%
 
Otros Químicos2601.3%
 
Otras Drogas2411.2%
 
Otros medios N/E1810.9%
 
Plaguicidas1670.9%
 
Arma Cortante1640.8%
 
Drogas Antiepilépticas1360.7%
 
Gases y Vapores920.5%
 
Por Fuego910.5%
 
Colisión Vehícular680.4%
 
Atropello160.1%
 
Narcóticos130.1%
 
Explosivos110.1%
 
Alcohol6< 0.1%
 
Analgésicos No Narcóticos5< 0.1%
 
Disolventes3< 0.1%
 
Por Objetos Calientes2< 0.1%
 
Drogas SNA2< 0.1%
 
Objeto Romo2< 0.1%
 
2020-09-18T10:50:11.332859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:11.458404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length12
Mean length12.42511253
Min length7

Overview of Unicode Properties

Unique unicode characters39
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o3568514.9%
 
e202808.4%
 
a199348.3%
 
r199158.3%
 
i187807.8%
 
t183777.7%
 
A181987.6%
 
m181067.5%
 
c177307.4%
 
n170607.1%
 
h163866.8%
 
46381.9%
 
s22090.9%
 
u21430.9%
 
d19050.8%
 
g18290.8%
 
F12780.5%
 
l8740.4%
 
p7610.3%
 
O6860.3%
 
P6300.3%
 
ó4560.2%
 
D3820.2%
 
í3280.1%
 
Q2600.1%
 
Other values (14)13350.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter21302488.7%
 
Uppercase Letter223229.3%
 
Space Separator46381.9%
 
Other Punctuation1810.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1819881.5%
 
F12785.7%
 
O6863.1%
 
P6302.8%
 
D3821.7%
 
Q2601.2%
 
C2341.0%
 
N2060.9%
 
E1920.9%
 
V1600.7%
 
G920.4%
 
R2< 0.1%
 
S2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o3568516.8%
 
e202809.5%
 
a199349.4%
 
r199159.3%
 
i187808.8%
 
t183778.6%
 
m181068.5%
 
c177308.3%
 
n170608.0%
 
h163867.7%
 
s22091.0%
 
u21431.0%
 
d19050.9%
 
g18290.9%
 
l8740.4%
 
p7610.4%
 
ó4560.2%
 
í3280.2%
 
é1410.1%
 
y92< 0.1%
 
v14< 0.1%
 
x11< 0.1%
 
b4< 0.1%
 
j4< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4638100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/181100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin23534698.0%
 
Common48192.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o3568515.2%
 
e202808.6%
 
a199348.5%
 
r199158.5%
 
i187808.0%
 
t183777.8%
 
A181987.7%
 
m181067.7%
 
c177307.5%
 
n170607.2%
 
h163867.0%
 
s22090.9%
 
u21430.9%
 
d19050.8%
 
g18290.8%
 
F12780.5%
 
l8740.4%
 
p7610.3%
 
O6860.3%
 
P6300.3%
 
ó4560.2%
 
D3820.2%
 
í3280.1%
 
Q2600.1%
 
C2340.1%
 
Other values (12)9200.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
463896.2%
 
/1813.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII23924099.6%
 
None9250.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o3568514.9%
 
e202808.5%
 
a199348.3%
 
r199158.3%
 
i187807.8%
 
t183777.7%
 
A181987.6%
 
m181067.6%
 
c177307.4%
 
n170607.1%
 
h163866.8%
 
46381.9%
 
s22090.9%
 
u21430.9%
 
d19050.8%
 
g18290.8%
 
F12780.5%
 
l8740.4%
 
p7610.3%
 
O6860.3%
 
P6300.3%
 
D3820.2%
 
Q2600.1%
 
C2340.1%
 
N2060.1%
 
Other values (11)7540.3%
 

Most frequent None characters

ValueCountFrequency (%) 
ó45649.3%
 
í32835.5%
 
é14115.2%
 

AT_MEDICA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
9
10951 
2
7481 
1
 
897
ValueCountFrequency (%) 
91095156.7%
 
2748138.7%
 
18974.6%
 
2020-09-18T10:50:11.566400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:11.626835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:11.704813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
91095156.7%
 
2748138.7%
 
18974.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
91095156.7%
 
2748138.7%
 
18974.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
91095156.7%
 
2748138.7%
 
18974.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
91095156.7%
 
2748138.7%
 
18974.6%
 

CAL_MEDICO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
2
18933 
3
 
302
1
 
94
ValueCountFrequency (%) 
21893398.0%
 
33021.6%
 
1940.5%
 
2020-09-18T10:50:11.808932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:11.880872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:11.957077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
21893398.0%
 
33021.6%
 
1940.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
21893398.0%
 
33021.6%
 
1940.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
21893398.0%
 
33021.6%
 
1940.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
21893398.0%
 
33021.6%
 
1940.5%
 

COD_MENOR
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
0
19329 
ValueCountFrequency (%) 
019329100.0%
 
2020-09-18T10:50:12.020806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

DIA_NAC.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.567541
Minimum0
Maximum31
Zeros2
Zeros (%)< 0.1%
Memory size151.1 KiB
2020-09-18T10:50:12.085176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.768529562
Coefficient of variation (CV)0.5632572005
Kurtosis-1.184671108
Mean15.567541
Median Absolute Deviation (MAD)8
Skewness0.0258259228
Sum300905
Variance76.88711068
MonotocityNot monotonic
2020-09-18T10:50:12.191659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
107714.0%
 
17353.8%
 
157293.8%
 
207113.7%
 
56703.5%
 
256683.5%
 
66663.4%
 
86633.4%
 
76493.4%
 
176443.3%
 
186423.3%
 
266353.3%
 
96283.2%
 
166183.2%
 
246173.2%
 
116153.2%
 
226143.2%
 
286093.2%
 
276053.1%
 
26053.1%
 
196053.1%
 
146013.1%
 
126003.1%
 
46003.1%
 
135973.1%
 
Other values (7)323216.7%
 
ValueCountFrequency (%) 
02< 0.1%
 
17353.8%
 
26053.1%
 
35672.9%
 
46003.1%
 
56703.5%
 
66663.4%
 
76493.4%
 
86633.4%
 
96283.2%
 
ValueCountFrequency (%) 
313201.7%
 
305743.0%
 
295923.1%
 
286093.2%
 
276053.1%
 
266353.3%
 
256683.5%
 
246173.2%
 
235923.1%
 
226143.2%
 

MES_NAC.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.734699157
Minimum0
Maximum12
Zeros2
Zeros (%)< 0.1%
Memory size151.1 KiB
2020-09-18T10:50:12.292873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.463052509
Coefficient of variation (CV)0.5142104241
Kurtosis-1.206179845
Mean6.734699157
Median Absolute Deviation (MAD)3
Skewness-0.1191701935
Sum130175
Variance11.99273268
MonotocityNot monotonic
2020-09-18T10:50:12.391998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
1018299.5%
 
918099.4%
 
817549.1%
 
1217298.9%
 
1117018.8%
 
116098.3%
 
615658.1%
 
715388.0%
 
314837.7%
 
514777.6%
 
414247.4%
 
214097.3%
 
02< 0.1%
 
ValueCountFrequency (%) 
02< 0.1%
 
116098.3%
 
214097.3%
 
314837.7%
 
414247.4%
 
514777.6%
 
615658.1%
 
715388.0%
 
817549.1%
 
918099.4%
 
ValueCountFrequency (%) 
1217298.9%
 
1117018.8%
 
1018299.5%
 
918099.4%
 
817549.1%
 
715388.0%
 
615658.1%
 
514777.6%
 
414247.4%
 
314837.7%
 

ANO1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.352579
Minimum2006
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:12.484837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12008
median2010
Q32013
95-th percentile2015
Maximum2015
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.870012355
Coefficient of variation (CV)0.001427616422
Kurtosis-1.223166562
Mean2010.352579
Median Absolute Deviation (MAD)2
Skewness0.09535227155
Sum38858105
Variance8.23697092
MonotocityIncreasing
2020-09-18T10:50:12.566957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2007216611.2%
 
2008214811.1%
 
2010202710.5%
 
2009200110.4%
 
2006192610.0%
 
201518589.6%
 
201118419.5%
 
201418359.5%
 
201317899.3%
 
201217389.0%
 
ValueCountFrequency (%) 
2006192610.0%
 
2007216611.2%
 
2008214811.1%
 
2009200110.4%
 
2010202710.5%
 
201118419.5%
 
201217389.0%
 
201317899.3%
 
201418359.5%
 
201518589.6%
 
ValueCountFrequency (%) 
201518589.6%
 
201418359.5%
 
201317899.3%
 
201217389.0%
 
201118419.5%
 
2010202710.5%
 
2009200110.4%
 
2008214811.1%
 
2007216611.2%
 
2006192610.0%
 

ANO2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.352579
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:12.652573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2012
Q32015
95-th percentile2017
Maximum2017
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.870012355
Coefficient of variation (CV)0.001426197569
Kurtosis-1.223166562
Mean2012.352579
Median Absolute Deviation (MAD)2
Skewness0.09535227155
Sum38896763
Variance8.23697092
MonotocityIncreasing
2020-09-18T10:50:12.734683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2009216611.2%
 
2010214811.1%
 
2012202710.5%
 
2011200110.4%
 
2008192610.0%
 
201718589.6%
 
201318419.5%
 
201618359.5%
 
201517899.3%
 
201417389.0%
 
ValueCountFrequency (%) 
2008192610.0%
 
2009216611.2%
 
2010214811.1%
 
2011200110.4%
 
2012202710.5%
 
201318419.5%
 
201417389.0%
 
201517899.3%
 
201618359.5%
 
201718589.6%
 
ValueCountFrequency (%) 
201718589.6%
 
201618359.5%
 
201517899.3%
 
201417389.0%
 
201318419.5%
 
2012202710.5%
 
2011200110.4%
 
2010214811.1%
 
2009216611.2%
 
2008192610.0%
 

DIA_DEF.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.459672
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:12.828677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.852605637
Coefficient of variation (CV)0.572625709
Kurtosis-1.204810612
Mean15.459672
Median Absolute Deviation (MAD)8
Skewness0.0260246801
Sum298820
Variance78.36862657
MonotocityNot monotonic
2020-09-18T10:50:12.935634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
17213.7%
 
37003.6%
 
26973.6%
 
196783.5%
 
66663.4%
 
246553.4%
 
96543.4%
 
116533.4%
 
266533.4%
 
56473.3%
 
226473.3%
 
166423.3%
 
76403.3%
 
46383.3%
 
186373.3%
 
216373.3%
 
86363.3%
 
256363.3%
 
206323.3%
 
106163.2%
 
236143.2%
 
146113.2%
 
136093.2%
 
176033.1%
 
155893.0%
 
Other values (6)321816.6%
 
ValueCountFrequency (%) 
17213.7%
 
26973.6%
 
37003.6%
 
46383.3%
 
56473.3%
 
66663.4%
 
76403.3%
 
86363.3%
 
96543.4%
 
106163.2%
 
ValueCountFrequency (%) 
313711.9%
 
305672.9%
 
295312.7%
 
285793.0%
 
275813.0%
 
266533.4%
 
256363.3%
 
246553.4%
 
236143.2%
 
226473.3%
 

MES_DEF.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.598685912
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:13.039106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.554361468
Coefficient of variation (CV)0.5386468631
Kurtosis-1.273037101
Mean6.598685912
Median Absolute Deviation (MAD)3
Skewness-0.06200530247
Sum127546
Variance12.63348544
MonotocityNot monotonic
2020-09-18T10:50:13.128032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
118979.8%
 
1217959.3%
 
1017409.0%
 
917288.9%
 
1117238.9%
 
315928.2%
 
815658.1%
 
515398.0%
 
414567.5%
 
214517.5%
 
714297.4%
 
614147.3%
 
ValueCountFrequency (%) 
118979.8%
 
214517.5%
 
315928.2%
 
414567.5%
 
515398.0%
 
614147.3%
 
714297.4%
 
815658.1%
 
917288.9%
 
1017409.0%
 
ValueCountFrequency (%) 
1217959.3%
 
1117238.9%
 
1017409.0%
 
917288.9%
 
815658.1%
 
714297.4%
 
614147.3%
 
515398.0%
 
414567.5%
 
315928.2%
 

ANO_DEF.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.352579
Minimum2007
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:13.221617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12009
median2011
Q32014
95-th percentile2016
Maximum2016
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.870012355
Coefficient of variation (CV)0.001426906642
Kurtosis-1.223166562
Mean2011.352579
Median Absolute Deviation (MAD)2
Skewness0.09535227155
Sum38877434
Variance8.23697092
MonotocityIncreasing
2020-09-18T10:50:13.301467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2008216611.2%
 
2009214811.1%
 
2011202710.5%
 
2010200110.4%
 
2007192610.0%
 
201618589.6%
 
201218419.5%
 
201518359.5%
 
201417899.3%
 
201317389.0%
 
ValueCountFrequency (%) 
2007192610.0%
 
2008216611.2%
 
2009214811.1%
 
2010200110.4%
 
2011202710.5%
 
201218419.5%
 
201317389.0%
 
201417899.3%
 
201518359.5%
 
201618589.6%
 
ValueCountFrequency (%) 
201618589.6%
 
201518359.5%
 
201417899.3%
 
201317389.0%
 
201218419.5%
 
2011202710.5%
 
2010200110.4%
 
2009214811.1%
 
2008216611.2%
 
2007192610.0%
 

FechaA
Date

Distinct3636
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Minimum2005-11-30 00:00:00
Maximum2015-12-31 00:00:00
2020-09-18T10:50:13.403465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:13.521671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FechaD
Date

Distinct3636
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Minimum2006-11-30 00:00:00
Maximum2016-12-31 00:00:00
2020-09-18T10:50:13.649519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:13.767005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3636
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Minimum2007-11-30 00:00:00
Maximum2017-12-31 00:00:00
2020-09-18T10:50:13.897457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:14.024642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3634
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
Minimum2007-01-01 00:00:00
Maximum2016-12-31 00:00:00
2020-09-18T10:50:14.152132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:14.268515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

difa
Real number (ℝ)

HIGH CORRELATION

Distinct720
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-361.0495111
Minimum-729
Maximum-1
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:14.395310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-729
5-th percentile-617
Q1-467
median-361
Q3-253
95-th percentile-108
Maximum-1
Range728
Interquartile range (IQR)214

Descriptive statistics

Standard deviation151.1715106
Coefficient of variation (CV)-0.4187002225
Kurtosis-0.5914062506
Mean-361.0495111
Median Absolute Deviation (MAD)107
Skewness-0.02545592373
Sum-6978726
Variance22852.82562
MonotocityNot monotonic
2020-09-18T10:50:14.521734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-365740.4%
 
-362710.4%
 
-367630.3%
 
-338620.3%
 
-357610.3%
 
-326610.3%
 
-366610.3%
 
-320610.3%
 
-363600.3%
 
-331600.3%
 
-392600.3%
 
-380590.3%
 
-356580.3%
 
-378580.3%
 
-371570.3%
 
-379560.3%
 
-374560.3%
 
-340560.3%
 
-332550.3%
 
-404550.3%
 
-397550.3%
 
-398550.3%
 
-373550.3%
 
-427550.3%
 
-376550.3%
 
Other values (695)1785092.3%
 
ValueCountFrequency (%) 
-7291< 0.1%
 
-7281< 0.1%
 
-7271< 0.1%
 
-7261< 0.1%
 
-7244< 0.1%
 
-7232< 0.1%
 
-7201< 0.1%
 
-7191< 0.1%
 
-7173< 0.1%
 
-7161< 0.1%
 
ValueCountFrequency (%) 
-11< 0.1%
 
-72< 0.1%
 
-82< 0.1%
 
-91< 0.1%
 
-102< 0.1%
 
-114< 0.1%
 
-126< 0.1%
 
-132< 0.1%
 
-144< 0.1%
 
-155< 0.1%
 

difb
Real number (ℝ)

HIGH CORRELATION

Distinct721
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.239174298
Minimum-364
Maximum364
Zeros77
Zeros (%)0.4%
Memory size151.1 KiB
2020-09-18T10:50:14.657583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-364
5-th percentile-251
Q1-102
median4
Q3113
95-th percentile257
Maximum364
Range728
Interquartile range (IQR)215

Descriptive statistics

Standard deviation151.190752
Coefficient of variation (CV)35.66514169
Kurtosis-0.5911244413
Mean4.239174298
Median Absolute Deviation (MAD)107
Skewness-0.02563747699
Sum81939
Variance22858.64349
MonotocityNot monotonic
2020-09-18T10:50:14.786630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0770.4%
 
3680.4%
 
-2670.3%
 
39650.3%
 
27630.3%
 
-13600.3%
 
-27600.3%
 
-6590.3%
 
10590.3%
 
-15580.3%
 
-12570.3%
 
33570.3%
 
34570.3%
 
-8570.3%
 
4570.3%
 
-9570.3%
 
25560.3%
 
9560.3%
 
2560.3%
 
66560.3%
 
5550.3%
 
51550.3%
 
-17550.3%
 
-5550.3%
 
16550.3%
 
Other values (696)1785292.4%
 
ValueCountFrequency (%) 
-3641< 0.1%
 
-3631< 0.1%
 
-3611< 0.1%
 
-3601< 0.1%
 
-3594< 0.1%
 
-3581< 0.1%
 
-3571< 0.1%
 
-3542< 0.1%
 
-3523< 0.1%
 
-3511< 0.1%
 
ValueCountFrequency (%) 
3641< 0.1%
 
3591< 0.1%
 
3582< 0.1%
 
3571< 0.1%
 
3562< 0.1%
 
3552< 0.1%
 
3548< 0.1%
 
3531< 0.1%
 
3523< 0.1%
 
3514< 0.1%
 

difc
Real number (ℝ≥0)

HIGH CORRELATION

Distinct721
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369.5395002
Minimum1
Maximum729
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:14.920919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile114
Q1263
median369
Q3478
95-th percentile622
Maximum729
Range728
Interquartile range (IQR)215

Descriptive statistics

Standard deviation151.187793
Coefficient of variation (CV)0.4091248512
Kurtosis-0.591195799
Mean369.5395002
Median Absolute Deviation (MAD)107
Skewness-0.02554802518
Sum7142829
Variance22857.74876
MonotocityNot monotonic
2020-09-18T10:50:15.047834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
365700.4%
 
400690.4%
 
356640.3%
 
350640.3%
 
369630.3%
 
353630.3%
 
411620.3%
 
374620.3%
 
368610.3%
 
364610.3%
 
367600.3%
 
333590.3%
 
372590.3%
 
375590.3%
 
392590.3%
 
359570.3%
 
405570.3%
 
409560.3%
 
391560.3%
 
390560.3%
 
338550.3%
 
303550.3%
 
339550.3%
 
355550.3%
 
363550.3%
 
Other values (696)1783792.3%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
41< 0.1%
 
51< 0.1%
 
62< 0.1%
 
73< 0.1%
 
81< 0.1%
 
112< 0.1%
 
132< 0.1%
 
142< 0.1%
 
ValueCountFrequency (%) 
7291< 0.1%
 
7241< 0.1%
 
7232< 0.1%
 
7222< 0.1%
 
7211< 0.1%
 
7202< 0.1%
 
7199< 0.1%
 
7181< 0.1%
 
7173< 0.1%
 
7166< 0.1%
 

dist_absoluta
Real number (ℝ≥0)

HIGH CORRELATION

Distinct184
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.43442496
Minimum0
Maximum183
Zeros77
Zeros (%)0.4%
Memory size151.1 KiB
2020-09-18T10:50:15.199928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q145
median91
Q3136
95-th percentile173
Maximum183
Range183
Interquartile range (IQR)91

Descriptive statistics

Standard deviation52.75143254
Coefficient of variation (CV)0.5833114167
Kurtosis-1.199352976
Mean90.43442496
Median Absolute Deviation (MAD)46
Skewness0.01049557407
Sum1748007
Variance2782.713635
MonotocityNot monotonic
2020-09-18T10:50:15.324226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
271320.7%
 
391290.7%
 
21240.6%
 
331240.6%
 
1251240.6%
 
1061230.6%
 
1821230.6%
 
1161220.6%
 
1101220.6%
 
1541210.6%
 
631210.6%
 
1131200.6%
 
1051200.6%
 
991180.6%
 
1341180.6%
 
1231180.6%
 
1771170.6%
 
581170.6%
 
121170.6%
 
1411170.6%
 
1261160.6%
 
1271160.6%
 
161160.6%
 
691160.6%
 
1031150.6%
 
Other values (159)1632384.4%
 
ValueCountFrequency (%) 
0770.4%
 
1950.5%
 
21240.6%
 
31100.6%
 
41120.6%
 
51110.6%
 
61130.6%
 
71030.5%
 
81120.6%
 
91140.6%
 
ValueCountFrequency (%) 
183190.1%
 
1821230.6%
 
1811070.6%
 
1801020.5%
 
179750.4%
 
178930.5%
 
1771170.6%
 
176930.5%
 
175930.5%
 
1741100.6%
 

distancia_final
Real number (ℝ)

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.160070361
Minimum-183
Maximum182
Zeros77
Zeros (%)0.4%
Memory size151.1 KiB
2020-09-18T10:50:15.454762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-183
5-th percentile-163
Q1-89
median1
Q392
95-th percentile164
Maximum182
Range365
Interquartile range (IQR)181

Descriptive statistics

Standard deviation104.6908599
Coefficient of variation (CV)90.24526744
Kurtosis-1.18571547
Mean1.160070361
Median Absolute Deviation (MAD)91
Skewness-0.01514664363
Sum22423
Variance10960.17616
MonotocityNot monotonic
2020-09-18T10:50:15.581533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0770.4%
 
156720.4%
 
113710.4%
 
39700.4%
 
-141700.4%
 
142690.4%
 
3680.4%
 
-2670.3%
 
182670.3%
 
-27660.3%
 
27660.3%
 
-123660.3%
 
-103660.3%
 
-61650.3%
 
110640.3%
 
-116640.3%
 
106640.3%
 
125630.3%
 
-99630.3%
 
80630.3%
 
140630.3%
 
-63630.3%
 
-105630.3%
 
100630.3%
 
52630.3%
 
Other values (341)1767391.4%
 
ValueCountFrequency (%) 
-183190.1%
 
-182560.3%
 
-181550.3%
 
-180580.3%
 
-179410.2%
 
-178480.2%
 
-177630.3%
 
-176420.2%
 
-175440.2%
 
-174480.2%
 
ValueCountFrequency (%) 
182670.3%
 
181520.3%
 
180440.2%
 
179340.2%
 
178450.2%
 
177540.3%
 
176510.3%
 
175490.3%
 
174620.3%
 
173530.3%
 

mes_suicidio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497283874
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size151.1 KiB
2020-09-18T10:50:15.687249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.722196761
Coefficient of variation (CV)0.4924383673
Kurtosis-1.284132274
Mean3.497283874
Median Absolute Deviation (MAD)2
Skewness0.003717612907
Sum67599
Variance2.965961683
MonotocityNot monotonic
2020-09-18T10:50:15.774484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
6332617.2%
 
1331117.1%
 
4324416.8%
 
2322016.7%
 
5311616.1%
 
3311216.1%
 
ValueCountFrequency (%) 
1331117.1%
 
2322016.7%
 
3311216.1%
 
4324416.8%
 
5311616.1%
 
6332617.2%
 
ValueCountFrequency (%) 
6332617.2%
 
5311616.1%
 
4324416.8%
 
3311216.1%
 
2322016.7%
 
1331117.1%
 

bimestre
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.1 KiB
1
6531 
3
6442 
2
6356 
ValueCountFrequency (%) 
1653133.8%
 
3644233.3%
 
2635632.9%
 
2020-09-18T10:50:15.872775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T10:50:15.940021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:16.017620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1653133.8%
 
3644233.3%
 
2635632.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19329100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1653133.8%
 
3644233.3%
 
2635632.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19329100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1653133.8%
 
3644233.3%
 
2635632.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19329100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1653133.8%
 
3644233.3%
 
2635632.9%
 

Interactions

2020-09-18T10:48:48.833005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:48.976678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.085184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.201362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.308031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.410700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.529934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.638688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.743437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.846954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:49.954094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.061496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.173351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.403543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.518544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.621507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.726426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.834330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:50.938307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.041809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.158085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.278490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.388636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.500377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.614879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.720270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.827353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:51.931662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.043032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.148300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.249381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.361642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.467623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.570424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.672261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.778160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.886229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:52.994120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.101374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.206916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.309044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.411749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.517562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.620977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.837941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:53.953969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.066941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.176571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.286124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.398556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.503049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.619241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.732281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.850705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:54.962491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.073336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.191771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.306863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.416114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.526999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.641358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.755338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.869718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:55.982904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.097898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.206186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.315719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.429816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.541548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.652201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.769911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:56.889871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.007635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.125235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.244427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.355836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.457552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.558509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.668821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.770013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:57.867190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.120143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.229763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.329401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.428154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.532279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.635282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.740948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.843227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:58.945432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.042735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.140755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.244245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.346065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.444924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.552093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.660359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.766537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.873522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:48:59.982060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.084700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.183632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.283344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.393256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.489875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.583639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.686702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.787679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.884275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:00.979520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.078733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.178338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.280757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.382964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.479907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.577473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.673059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.772204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.869228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:01.964758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.067094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.172206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.274122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.377798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.487788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.586471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.700586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.816321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:02.937682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.224749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.338724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.459294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.572901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.683776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.793167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:03.905622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.020098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.135973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.249414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.363340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.471976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.583971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.697030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.807840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:04.916662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.036024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.155176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.271762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.391336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.510126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.622968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.729653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.836509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:05.948918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.051769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.154467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.266189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.372309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.476483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.579502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.689668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.798994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:06.909176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.015373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.121116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.224828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.326213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.432728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.534970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.638767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.750218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.863118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:07.972361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.082298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.195264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.299151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.402681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.504291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.616290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.718087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.820279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:08.928202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.032280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.132876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.231657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.554732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.665073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.770384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.876269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:09.978761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:10.077883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T10:49:56.063810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.166119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.276949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.377522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.477426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.607590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.711154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.811631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:56.919489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.032093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.141376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.246110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.349632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.453327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.554377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.655997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.795573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.896866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:57.997529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:58.110803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:58.220649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:58.326653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:58.434206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:49:58.546969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-18T10:50:16.160173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-18T10:50:16.496315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-18T10:50:16.823940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-18T10:50:17.169842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-18T10:50:17.526314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-18T10:49:58.945193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T10:50:01.824519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

DIA_NACMES_NACAÑONACIMANO1_NACANO2_NACSEXOEST_CIVILEDAD_TIPOEDAD_CANTRENGOETARIO_NRANGO ETARIOEDAD_LABORALCURSO_INSNIVEL_INSACTIVIDADOCUPACIONCATEGORIAFECHA_DEFESTACION NESTACIONDIA_SEMANADIA_DEFMES_DEFANO_DEFLUGAR_DEFREG_RESSERV_RESCOMUNADESC_COMUNAURB_RURALDIAG1DIAG2MEC_SUICIDASUBTIPO_MECAT_MEDICACAL_MEDICOCOD_MENORDIA_NAC.1MES_NAC.1ANO1ANO2DIA_DEF.1MES_DEF.1ANO_DEF.1FechaAFechaDFechaDespFECHA_DEF.1difadifbdifcdist_absolutadistancia_finalmes_suicidiobimestre
013419851985211213adulto jovenEdadLab_MED310302007-01-031VERANOmiércoles3120072121101ARICA1T71XX700LesiónAhorcamiento120134200620083120072006-04-132007-04-132008-04-132007-01-03-26510046610010042
1171219751975111313adulto jovenEdadLab_MED641742007-01-071VERANOdomingo7120072121101ARICA1T71XX700LesiónAhorcamiento2201712200620087120072006-12-172007-12-172008-12-172007-01-07-2134471021-2111
29619561956121514adultoEdadLab_MAX421522007-09-164INVIERNOdomingo16920073121107CAMARONES1T293X768LesiónPor Fuego220962006200816920072006-06-092007-06-092008-06-092007-09-16-464-9926799-9942
328919831983111233adulto jovenEdadLab_MED421X22007-01-021VERANOmartes2120071232101TOCOPILLA1T71XX700LesiónAhorcamiento120289200620082120072006-09-282007-09-282008-09-282007-01-02-9626963596-9642
424419841984111223adulto jovenEdadLab_MED421722007-01-101VERANOmiércoles10120071232101TOCOPILLA1S062X740LesiónArma de Fuego1202442006200810120072006-04-242007-04-242008-04-242007-01-10-26110447010410442
57219621962111444adultoEdadLab_MAX421732007-01-101VERANOmiércoles10120073232101TOCOPILLA1T012X788LesiónArma Cortante220722006200810120072006-02-072007-02-072008-02-072007-01-10-33728393282811
62441999199921171niño(a)niño(a)340302007-01-131VERANOsábado13120072232101TOCOPILLA1T71XX700LesiónAhorcamiento2202442006200813120072006-04-242007-04-242008-04-242007-01-13-26410146710110142
714319711971121353adulto jovenEdadLab_MED220802007-01-191VERANOviernes19120072232101TOCOPILLA1T71XX700LesiónAhorcamiento2201432006200819120072006-03-142007-03-142008-03-142007-01-19-31154420545421
816719871987111192AdolescenteEdadLab_TEM220302007-03-201VERANOmartes20320072121101ARICA1T71XX700LesiónAhorcamiento2201672006200820320072006-07-162007-07-162008-07-162007-03-20-24711848411811842
921119791979111273adulto jovenEdadLab_MED421722007-02-151VERANOjueves15220073232101TOCOPILLA1T71XX701LesiónAhorcamiento2202112006200815220072006-11-022007-11-022008-11-022007-02-15-105260626105-10542

Last rows

DIA_NACMES_NACAÑONACIMANO1_NACANO2_NACSEXOEST_CIVILEDAD_TIPOEDAD_CANTRENGOETARIO_NRANGO ETARIOEDAD_LABORALCURSO_INSNIVEL_INSACTIVIDADOCUPACIONCATEGORIAFECHA_DEFESTACION NESTACIONDIA_SEMANADIA_DEFMES_DEFANO_DEFLUGAR_DEFREG_RESSERV_RESCOMUNADESC_COMUNAURB_RURALDIAG1DIAG2MEC_SUICIDASUBTIPO_MECAT_MEDICACAL_MEDICOCOD_MENORDIA_NAC.1MES_NAC.1ANO1ANO2DIA_DEF.1MES_DEF.1ANO_DEF.1FechaAFechaDFechaDespFECHA_DEF.1difadifbdifcdist_absolutadistancia_finalmes_suicidiobimestre
1931922119781978111383adulto jovenEdadLab_MED441532016-08-274INVIERNOsábado278201638178401LOS ANGELES1T58XX678EnvenenamientoGases y Vapores9202212015201727820162015-01-222016-01-222017-01-222016-08-27-583-21814814814853
1932023819471947131695adulto mayorEdad_AVAN051642016-08-284INVIERNOdomingo288201638178401LOS ANGELES2T609X689EnvenenamientoPlaguicidas9202382015201728820162015-08-232016-08-232017-08-232016-08-28-371-53605-511
1932121819731973111434adultoEdadLab_MAX841632016-08-294INVIERNOlunes298201638178416ALTO BIOBIO2T71XX708LesiónAhorcamiento9202182015201729820162015-08-212016-08-212017-08-212016-08-29-374-83578-811
1932228719861986121303adulto jovenEdadLab_MED421522016-08-314INVIERNOmiércoles318201638178405NACIMIENTO1T71XX708LesiónAhorcamiento9202872015201731820162015-07-282016-07-282017-07-282016-08-31-400-3433134-3421
1932317819561956121604adultoEdadLab_MAX841542016-08-304INVIERNOmartes308201628288201CONCEPCION1T71XX709LesiónAhorcamiento9201782015201730820162015-08-172016-08-172017-08-172016-08-30-379-1335213-1311
193241519411941211755adulto mayorEdad_AVAN510502016-08-254INVIERNOjueves258201629219108LUMACO2T58XX670EnvenenamientoGases y Vapores920152015201725820162015-05-012016-05-012017-05-012016-08-25-482-116249116-11642
1932511419681968111484adultoEdadLab_MAX511722016-08-294INVIERNOlunes298201639219115LONQUIMAY2T71XX708LesiónAhorcamiento9201142015201729820162015-04-112016-04-112017-04-112016-08-29-506-140225140-14053
193263319801980111363adulto jovenEdadLab_MED841x42016-08-274INVIERNOsábado27820162142214202LAMPA1T71XX700LesiónAhorcamiento220332015201727820162015-03-032016-03-032017-03-032016-08-27-543-177188177-17763
19327221219771977211383adulto jovenEdadLab_MED221732016-08-284INVIERNOdomingo28820163102410101VALDIVIA2T71XX708LesiónAhorcamiento92022122015201728820162015-12-222016-12-222017-12-222016-08-28-25011648111611642
19328161019891989111263adulto jovenEdadLab_MED421622016-08-304INVIERNOmartes30820163103310203PUERTO OCTAY2T71XX708LesiónAhorcamiento92016102015201730820162015-10-162016-10-162017-10-162016-08-30-31947412474721